Descriptor-guided thermodynamic screening of H₂ adsorption on single-atom-doped anatase TiO₂ nanoparticles with interpretable machine learning

Understanding how surface dopants tune H₂ adsorption on oxide nanoparticles is important for the design of reversible hydrogen-storage materials and catalytic interfaces. Here, we present a descriptor-guided screening study of molecular H₂ adsorption on pristine and single-atom-doped anatase TiO₂ nanoparticles using density-functional tight-binding calculations, conceptual DFT descriptors, thermodynamic modelling, and interpretable machine learning. The replacement of one surface Ti atom with Al, Fe, Hf, La, Mo, Nb, Sn, V, W, or Zr enables systematic comparison across chemically distinct adsorption environments. Most dopants preserve molecular adsorption, whereas Fe shows incipient dissociative activation, and the adsorption energies span from -0.275 to -0.523 eV, indicating that single-atom doping can tune H₂ binding over a practically relevant range. Descriptor analysis separates weakly perturbed wide-gap systems from narrow-gap dopants with dopant-derived frontier states, enhanced softness, and higher electrophilicity. Symbolic regression with leave-one-out cross-validation identifies a compact ω⁻-dependent expression (the electron-donating power, a member of the electrophilicity descriptor family) as the most generalizing descriptor–property relationship in this small-data regime, with higher-complexity formulas exhibiting an overfitting cliff. Gaussian-process modelling is retained as an uncertainty-driven active-learning sampling-design tool. Thermodynamic screening further shows that Nb and Zr provide the most balanced uptake–release behavior, Sn remains borderline viable, and Hf and Mo define a stronger-binding but less balanced regime. Overall, the workflow provides a data-efficient and physically interpretable basis for screening dopant chemistry in oxide nanomaterials.